PYSEC-2026-2302
Vulnerability from pysec - Published: 2026-06-11 10:16 - Updated: 2026-07-13 05:52
VLAI
Details
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the VideoMediaIO.load_base64() method. When processing video/jpeg data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
Severity
7.5 (High)
Impacted products
| Name | purl | vllm | pkg:pypi/vllm |
|---|
Aliases
{
"affected": [
{
"ecosystem_specific": {},
"package": {
"ecosystem": "PyPI",
"name": "vllm",
"purl": "pkg:pypi/vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0.8.0"
},
{
"fixed": "0.19.0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.10.0",
"0.10.1",
"0.10.1.1",
"0.10.2",
"0.11.0",
"0.11.1",
"0.11.2",
"0.12.0",
"0.13.0",
"0.14.0",
"0.14.1",
"0.15.0",
"0.15.1",
"0.16.0",
"0.17.0",
"0.17.1",
"0.18.0",
"0.18.1",
"0.8.0",
"0.8.1",
"0.8.2",
"0.8.3",
"0.8.4",
"0.8.5",
"0.8.5.post1",
"0.9.0",
"0.9.0.1",
"0.9.1",
"0.9.2"
]
}
],
"aliases": [
"CVE-2026-5497"
],
"details": "vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.",
"id": "PYSEC-2026-2302",
"modified": "2026-07-13T05:52:25.563903Z",
"published": "2026-06-11T10:16:21.903Z",
"references": [
{
"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2026-5497"
},
{
"type": "WEB",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-5497.json"
},
{
"type": "REPORT",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2487813"
},
{
"type": "FIX",
"url": "https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395"
},
{
"type": "EVIDENCE",
"url": "https://huntr.com/bounties/7bd92629-b396-4449-8f88-6c0092530eb4"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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